I am co-author of a new publication in the field of metabolomics and metabolite identification. Its title is “Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees”
Miquel, Rojas-Cherto,; E, Peironcely, Julio; T, Kasper, Piotr; J, van der Hooft, Justin J; H, de Vos, Ric C; Rob, Vreeken,; Thomas, Hankemeier,; Theo, Reijmers,
Analytical chemistry, 2012.
Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data.
Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n).
For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree.
Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.
In Simple Words
Similar metabolites have similar mass spectral trees. Imagine you have a collection or database of mass spectral trees of known metabolites, i.e. for which you know the chemical structure.
Now you have a mass spectral tree of an unknown metabolite. We propose a method to use the similarity between trees to identify the unknown either:
- By finding in the database a tree that is 100% similar. Here we could assign the identity to the unknown because we had it in the database.
- We might find several similar metabolites (not 100%) in the database. If these metabolites belong to a sub-class, let’s say amino acids, we assign the class of the unknown, it should also be a amino acid.
- These similar metabolites can have a common piece (a ring or a scaffold) which we speculate that is also present in the structure of the unknown metabolite. This piece, the Maximum Common Substructure, can help in metabolite identification and be used in Computer Assisted Structure Elucidation process to propose candidate structures for the unknown that contain such piece.